Conventional wireless receivers often use noise and/or data covariance information in the form of a covariance matrix to suppress interference between multiple received signals, i.e., multiple sample sets of a single transmitted signal, multiple different signals, or any combination thereof. Examples of interference suppressing receivers include chip equalizers, RAKE receivers, Generalized RAKE (GRAKE) receivers, single-input multiple-output receivers, multiple-input multiple-output receivers, etc.
As is known in the art, multi-user detection (MUD) has been shown to be an effective way to suppress multiple-access interference (MAI) and to improve system capacity. In MUD systems, the signals from interfering users are used in the detection of individual user signals. Examples of MUD systems include interference subtraction receivers, often referred to as successive interference cancellation (SIC) receivers, and decision feedback (DF) receivers. The SIC approach is based on the idea that once a decision has been made about an interfering user's bit, then the interfering signal can be recreated at the receiver using knowledge of the channel and subtracted from the received signal. This process is repeated successively, for one or more other users' signals, and progressively reduces the interference as each of the signals associated with other users is detected. Often, the strongest signals are detected first and canceled from the received signal, which mitigates the interference for weaker signals.
The DF approach is based on a similar idea, except the subtraction is done on a processed version of the received signal, namely the receiver decision statistics. Furthermore, the subtracted quantity is formed from the previously detected user bits, in a similar manner as decision feedback equalization. While MUD systems are effective in reducing MAI, the complexity of optimal MUD systems increases exponentially with the number of users. Thus, most practical MUD systems use sub-optimal detection systems.
Interference suppressing receivers require accurate tracking of statistical properties of the received signal and/or signal impairments, typically using a covariance matrix. Tracking data or impairment covariances often requires highly complex computations due to the large number of received sample sets. These complex computations often restrict a wireless receiver's ability to accurately track and utilize the signal covariances. These problems may be exacerbated in interference subtraction receivers and other MUD receiver designs because of the need to calculate covariances each time a received signal is modified by subtracting an interfering user's signal.